Forecasting Short Term Wind Energy Generation using Machine Learning

Abstract

Wind power is very different from other sources due to its volatile and stochastic nature. The forecasting of wind energy generation is a very important aspect for the reliability and challenges regarding balancing the supply and demand in power systems. In this paper, Support Vector Machine (SVM) based regression algorithm is used for one day ahead prediction of wind energy production in Estonia. The proposed algorithm is then compared with the results of the prediction algorithm used by the Estonian energy regulatory organization. The results indicate that our proposed algorithms give better forecasting and the lowest Root Mean Square Error (RMSE) values.